AnswersSovereign AI
In short
Avoid AI lock-in by owning the parts that are expensive to rebuild: the model of your business, your instructions and evaluations, your fine-tuned models and your audit records. Route across more than one model family, run in your own cloud where you can, keep data out of suppliers' training by contract, and test that you can move a workflow before you depend on it.
| Where | How it locks you in | How to keep it portable |
|---|---|---|
| One model family | Your roadmap follows one lab's releases, prices and terms. | Route across every frontier lab and open-weight models. |
| Data terms | Retention and training terms change with a release. | Contracts that keep data out of training, and data that stays in your environment. |
| Instructions and evaluations | Stored in a vendor's format you cannot export. | Keep them as your own assets, in a portable form. |
| Fine-tuned models | The vendor keeps the weights, or retires the base model. | Train on open-weight bases and keep the weights. |
| Integrations | Every connection is rebuilt if you leave. | Connectors that inherit source permissions on a platform you control. |
| Records | Audit history sits in a vendor dashboard. | An exportable, queryable audit trail. |
The proof that you are not locked in is a rehearsal: restore one workflow in a second environment from the exported context, instructions and audit trail, with behavior and permissions intact. Run it before production and again after material changes.
CosmOS runs in your own cloud or Fig's, routes every task across every frontier lab and open-weight models, keeps the digital twin, instructions, evaluations and trained weights as your assets, and exports the full audit trail.
The best model for a task changes every few months, and suppliers change terms. Routing across models keeps quality up and cost down, and removes a single point of failure.
Read the contract. On many hosted services the provider keeps the weights. Training on open-weight bases in your environment keeps them yours.
Yes, if your context, instructions, evaluations, models and records are portable. Rehearse the move before you rely on it.
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